Pan-cancer image-based detection of clinically actionable genetic alterations proposed by Kather et al. [1]}. The idea is to take CNNs pre-trained on ImageNet, and apply transfer learning on the tumor patches. They showed that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures, directly from routine histological images of tumor tissue.
